Volume 2, Issue 2 — Year 2025 — Article e100042

ISSN (Online): 3115-8129 Biannually

Predicting Flood Energy Attenuation in Vegetated Rivers using Artificial Neural Networks (ANN)

Article Type: Research
Pages: e100042
DOI: https://doi.org/10.22034/CGEL.2.2.e100042
Crossmark
Open Access
Authors
Affiliations
  1. Soil Science Department, University of Tabriz, Tabriz 5166616471, Iran
  2. Department of Environmental Science, Tunghai University, Taichung 407224, Taiwan
  3. Department of Environmental Science, Tunghai University, Taichung 407224, Taiwan
  4. Department of Civil Engineering, I-Shou University, Kaohsiung 84001, Taiwan
  5. Department of Civil Engineering, I-Shou University, Kaohsiung 84001, Taiwan
Corresponding author
Email: yeganeh.ma@162.com
Received: 3 September 2025 / Accepted: 25 September 2025 / Published: 19 October 2025
Abstract

Flood energy attenuation in vegetated rivers is a critical factor in flood management and riverine ecosystem stability. This study develops an Artificial Neural Network (ANN) model to predict flood energy reduction using a dataset of 760 rivers in Iran. The dataset was divided into 70% for training and 30% for testing. A multi-layer perceptron (MLP) ANN was implemented in Python to establish the relationship between key hydraulic and vegetation parameters and energy dissipation. The input variables included the Froude number (Fr), vegetation density and thickness (Dv), and relative backwater rise (Δr), while the output parameter was energy reduction (ΔE). The model’s performance was evaluated using statistical metrics, achieving a high correlation (R² = 0.92) and a low mean absolute error (MAE = 0.025 and RMSE = 0.012), demonstrating the ANN’s strong predictive capability. Results indicate that vegetation characteristics significantly influence energy dissipation, with denser and thicker vegetation leading to greater flood energy reduction. Sensitivity analysis further highlighted the dominant role of Δr in determining energy loss. The ANN model outperformed traditional empirical methods in accuracy, proving its reliability for practical applications in flood risk assessment. These findings suggest that ANN-based modeling can be a valuable tool for hydrologists and engineers in optimizing river management strategies. Future research should focus on expanding the dataset and integrating additional hydraulic parameters to further refine prediction accuracy.

Keywords

Flood energy, Vegetated rivers, Artificial neural networks, Flood prediction, Hydraulic modeling

Data availability statement

The data supporting the findings of this study are available within article. No publicly archived dataset was generated.

Funding

This research received no external funding.

Highlights
  • ANN predicts flood energy reduction accurately
  • Vegetation density increases energy dissipation
  • Model supports flood risk and river management
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How to cite
Mohammadian, N., Rad, S., Fen, C., Wang, D., & Yeganeh, M. (2025). Predicting Flood Energy Attenuation in Vegetated Rivers using Artificial Neural Networks (ANN). Civil and Geoengineering Letters, 2(2), e100042. https://doi.org/10.22034/CGEL.2.2.e100042
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